The first functional load-carrying and energetically autonomous exoskeleton was demonstrated at the University of California, Berkeley, walking at the average speed of while carrying a payload. Four fundamental technologies associated with the Berkeley lower extremity exoskeleton were tackled during the course of this project. These four core technologies include the design of the exoskeleton architecture, control schemes, a body local area network to host the control algorithm, and a series of on-board power units to power the actuators, sensors, and the computers. This paper gives an overview of one of the control schemes. The analysis here is an extension of the classical definition of the sensitivity function of a system: the ability of a system to reject disturbances or the measure of system robustness. The control algorithm developed here increases the closed-loop system sensitivity to its wearer’s forces and torques without any measurement from the wearer (such as force, position, or electromyogram signal). The control method has little robustness to parameter variations and therefore requires a relatively good dynamic model of the system. The trade-offs between having sensors to measure human variables and the lack of robustness to parameter variation are described.
Skip Nav Destination
Article navigation
March 2006
Technical Papers
The Berkeley Lower Extremity Exoskeleton
H. Kazerooni,
H. Kazerooni
University of California
, Berkeley, Berkeley, CA 94720
Search for other works by this author on:
R. Steger
R. Steger
Search for other works by this author on:
H. Kazerooni
University of California
, Berkeley, Berkeley, CA 94720
R. Steger
J. Dyn. Sys., Meas., Control. Mar 2006, 128(1): 14-25 (12 pages)
Published Online: September 17, 2005
Article history
Received:
March 31, 2005
Revised:
September 17, 2005
Citation
Kazerooni, H., and Steger, R. (September 17, 2005). "The Berkeley Lower Extremity Exoskeleton." ASME. J. Dyn. Sys., Meas., Control. March 2006; 128(1): 14–25. https://doi.org/10.1115/1.2168164
Download citation file:
Get Email Alerts
Offline and Online Exergy-Based Strategies for Hybrid Electric Vehicles
J. Dyn. Sys., Meas., Control (May 2025)
Multi Combustor Turbine Engine Acceleration Process Control Law Design
J. Dyn. Sys., Meas., Control
A Distributed Layered Planning and Control Algorithm for Teams of Quadrupedal Robots: An Obstacle-Aware Nonlinear Model Predictive Control Approach
J. Dyn. Sys., Meas., Control (May 2025)
Active Data-Enabled Robot Learning of Elastic Workpiece Interactions
J. Dyn. Sys., Meas., Control (May 2025)
Related Articles
An Approach to Identify Joint Motions for Dynamically Stable Walking
J. Mech. Des (May,2006)
Search for Initial Conditions for Sustained Hopping of Passive Springy-Leg Offset-Mass Hopping Robot
J. Dyn. Sys., Meas., Control (July,2007)
Fault Tolerant Control and Classification for Longitudinal Vehicle Control
J. Dyn. Sys., Meas., Control (September,2003)
Probabilistic Control for Uncertain Systems
J. Dyn. Sys., Meas., Control (March,2012)
Related Proceedings Papers
Related Chapters
QP Based Encoder Feedback Control
Robot Manipulator Redundancy Resolution
Dynamic Simulations to Become Expert in Order to Set Fuzzy Rules in Real Systems
International Conference on Advanced Computer Theory and Engineering, 4th (ICACTE 2011)
EIFS: When It Works, When It Does Not
Exterior Insulation Finish Systems (EIFS): Materials, Properties, and Performance